The problems of noise removal, and simultaneous noise removal and deblurring of imagery are common to many areas of science. An approach which allows for the unified treatment of both problems involves modeling imagery as a sample of a random process. Various nonstationary image models are explored in this context. Attention is directed to identifying the model parameters from imagery which has been corrupted by noise and possibly blur, and the use of the model to form an optimal reconstruction of the image. Throughout the work, emphasis is placed on both theoretical development and practical considerations involved in achieving this reconstruction. The results indicate that the use of nonstationary image models offers considerable improvement over traditional techniques.

The problems of noise removal, and simultaneous noise removal and deblurring of imagery are common to many areas of science. An approach which allows for the unified treatment of both problems involves modeling imagery as a sample of a random process. Various nonstationary image models are explored in this context. Attention is directed to identifying the model parameters from imagery which has been corrupted by noise and possibly blur, and the use of the model to form an optimal reconstruction of the image. Throughout the work, emphasis is placed on both theoretical development and practical considerations involved in achieving this reconstruction. The results indicate that the use of nonstationary image models offers considerable improvement over traditional techniques.

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dc.type

text

en_US

dc.type

Dissertation-Reproduction (electronic)

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dc.subject

Image processing -- Digital techniques.

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thesis.degree.name

Ph.D.

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thesis.degree.level

doctoral

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thesis.degree.discipline

Electrical and Computer Engineering

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thesis.degree.discipline

Graduate College

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thesis.degree.grantor

University of Arizona

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dc.contributor.advisor

Hunt, Bobby R.

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dc.contributor.committeemember

Dudley, Donald G.

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dc.contributor.committeemember

Strickland, Robin R.

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dc.identifier.proquest

8514917

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dc.identifier.oclc

696347733

en_US

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